About Unconventional AI We are rethinking the foundations of the computer to optimize energy efficiency for AI. Founded by pioneers in the field - including Naveen Rao (Nervana, MosaicML) and Michael Carbin (MIT, MosaicML) - we are building a new computational substrate that interfaces directly with the physics of silicon to achieve biology-scale efficiency. We recently raised $475M in seed funding to turn this vision into reality. As a Junior Member of Technical Staff, System Modeling, you will work closely with senior engineers to contribute to the development of our multi-disciplinary simulation frameworks. You will assist the hands-on R&D team in building simulation environments that enable rapid iteration and testing across all layers of our unconventional physics-based computing systems for machine learning workloads. Your work will focus on integrating physics-based models, developing GPU-accelerated simulations, and supporting the cross-layer system integration necessary for "Extreme co-design".

Key Responsibilities

Contribute to the implementation and optimization of GPU-accelerated simulators for ML on analog/unconventional hardware, focusing on specific modules and features within PyTorch. Assist in integrating physics-based device and system models into the PyTorch simulation environment to help expose early algorithm–hardware tradeoffs and enable cross-layer optimization. Support the maintenance and extension of the unified end-to-end simulation environment, helping to link theory, algorithms, and device models, and ensuring alignment between high-level and near-physical simulators. Help implement and adhere to robust experiment tracking protocols to ensure simulation results, configurations, and non-idealities are reproducible and auditable. Collaborate with Algorithms and Hardware teams to gather requirements and ensure the modeling environment meets their needs for high-level algorithm development and lower-level hardware verification. What We’re Looking For Strong Systems Foundation: A BS, MS, or PhD in Computer Science, Electrical Engineering, or a related technical field. You should have a deep understanding of computer architecture and operating systems. Coding Proficiency: Strong skills in C++ and Python. You should be comfortable writing performance-critical code. AI/ML Exposure: Basic familiarity with the internals of deep learning frameworks (e.g., how a PyTorch graph is executed) and common model architectures. Mathematical Intuition: A solid grasp of linear algebra and calculus, which are essential for understanding both neural dynamics and hardware optimizations. First Principles Mindset: You enjoy digging into "why" things work (or don't) and aren't afraid to challenge conventional software "best practices" to find a more efficient path. Bonus Points Experience with compilers (LLVM, MLIR) or domain-specific languages like Triton. Exposure to GPU programming (CUDA) or other hardware accelerators. Prior research or internship experience in high-performance computing (HPC) or neuromorphic systems. Contributions to open-source AI or systems software projects. Why Join Us? Mentorship: Learn directly from the architects who built the modern AI stack at companies like Intel, Databricks, and NVIDIA. Impact: You won't be a small cog in a giant machine. You will be helping build the machine itself. Unconventional Problems: Work on challenges that don't have a StackOverflow answer—you’ll be defining the future of AI compute. Competitive Package: Significant equity and competitive salary at a well-funded, high-growth startup.